Delusive PageRank in Incomplete Graphs

Holzmann, Helge; Anand, Avishek; Khosla, Megha

doi:10.1007/978-3-030-05411-3_9

Cited by 10 publications

(14 citation statements)

References 19 publications

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“…We remark that the main results corresponding to RQ I and RQ II appeared in the preliminary version of the paper (Holzmann et al 2018). The present work in addition investigates RQ II towards which new algorithm is proposed and evaluated.…”

Section: Introductionmentioning

confidence: 86%

Estimating PageRank deviations in crawled graphs

2019

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Most real-world graphs collected from the Web like Web graphs and social network graphs are partially discovered or crawled. This leads to inaccurate estimates of graph properties based on link analysis such as PAGERANK. In this paper we focus on studying such deviations in ordering/ranking imposed by PAGERANK over crawled graphs. We first show that deviations in rankings induced by PAGERANK are indeed possible. We measure how much a ranking, induced by PAGERANK, on an input graph could deviate from the original unseen graph. More importantly, we are interested in conceiving a measure that approximates the rank correlation among them without any knowledge of the original graph. To this extent we formulate the HAK measure that is based on computing the impact redistribution of PAGERANK according to the local graph structure. We further propose an algorithm that identifies connected subgraphs over the input graph for which the relative ordering is preserved. Finally, we perform extensive experiments on both real-world Web and social network graphs with more than 100M vertices and 10B edges as well as synthetic graphs to showcase the utility of HAK and our High-fidelity Component Selection approach.

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Section: Introductionmentioning

confidence: 86%

Estimating PageRank deviations in crawled graphs

2019

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“…Given the differences in datasets, we expect differences in the derived social networks (Tables 3 and 4) [14]. Each network is dominated by a single large component, comprising over 90% of nodes in the retweet and mention networks, and around 70% in the reply networks.…”

Section: Comparison Of Network Statisticsmentioning

confidence: 99%

“…Previous work has considered the question of data reliability from a sampling perspective [4][5][6][7], biases [8][9][10][11] and the danger of making invalid generalisations using "big data" approaches lacking nuanced interpretation of the data [12,13]. Analyses of incomplete networks exist [14], but this paper specifically considers the questions of data reliability for SNA, considering not only the significance of online interactions to discover meaningful social networks, but also how sampling and boundary issues can complicate analyses of the networks constructed. Through an exploration of modelling and collection issues, and a measurement study examining the reliability of simultaneously collected, or parallel, datasets, this multidisciplinary study addresses the following research question: How do variations in collections affect the results of social network analyses?…”

Section: Introductionmentioning

confidence: 99%

A method to evaluate the reliability of social media data for social network analysis

Weber

Nasim

Mitchell

et al. 2020

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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“…In the same way as Kim & Jeong (2007) and Holzmann et al (2019), we use Kendall's τ ("tau-b") rank correlation coefficient (Kendall, 1945) to measure the robustness of centrality measures. In this case, the robustness is defined as follows:…”

Section: Robustness Of Centrality Measuresmentioning

confidence: 99%

“…1 Previous studies have used the Pearson correlation to measure the robustness (Bolland, 1988;Costenbader & Valente, 2003;Borgatti et al, 2006). Like most recent studies, we use a rank correlation (Kim & Jeong, 2007;Wang et al, 2012;Holzmann et al, 2019;Martin & Niemeyer, 2019). The effects of errors on the robustness of centrality measures depend on several variables, for example, the type of centrality measure, the type and extent of the error, the network topology (e.g., tree-like, core-periphery), and how we measure the robustness (Frantz et al, 2009;Smith & Moody, 2013).…”

Section: Introductionmentioning

confidence: 99%

On the impact of network size and average degree on the robustness of centrality measures

Martin¹,

Niemeyer²

2020

Net Sci

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Measurement errors are omnipresent in network data. Most studies observe an erroneous network instead of the desired error-free network. It is well known that such errors can have a severe impact on network metrics, especially on centrality measures: a central node in the observed network might be less central in the underlying, error-free network. The robustness is a common concept to measure these effects. Studies have shown that the robustness primarily depends on the centrality measure, the type of error (e.g., missing edges or missing nodes), and the network topology (e.g., tree-like, core-periphery). Previous findings regarding the influence of network size on the robustness are, however, inconclusive. We present empirical evidence and analytical arguments indicating that there exist arbitrary large robust and non-robust networks and that the average degree is well suited to explain the robustness. We demonstrate that networks with a higher average degree are often more robust. For the degree centrality and Erdős–Rényi (ER) graphs, we present explicit formulas for the computation of the robustness, mainly based on the joint distribution of node degrees and degree changes which allow us to analyze the robustness for ER graphs with a constant average degree or increasing average degree.

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Delusive PageRank in Incomplete Graphs

Cited by 10 publications

References 19 publications

Estimating PageRank deviations in crawled graphs

Estimating PageRank deviations in crawled graphs

A method to evaluate the reliability of social media data for social network analysis

On the impact of network size and average degree on the robustness of centrality measures

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